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Parenting Stress in Fathers: Do We Need Father Specific Reference Samples? And Do They Differ in Regard of Taking Parental Leave?

Nina KrügerJohanna Nuria Rüther
Published in: Children (Basel, Switzerland) (2022)
The German version of the Parenting stress Index from Abidin, the Eltern-Belastungs-Inventar (EBI) merely provides reference samples of 538 mothers of children in toddlers and preschool age. Although meant to measure parenting stress, there are no father specific reference samples provided. The aim was to investigate differences in parenting stress between fathers and the provided reference samples of German mothers. Furthermore, the aim was to examine potential differences in the perceived stress between fathers who did and those who did not take parental leave. A total of 497 fathers living in Germany, of which more than half took parental leave, filled out the questionnaire via an online survey or the paper-pencil-version. All fathers completed the EBI and provided socio-economic data. The collected data were analyzed in terms of test quality, such as mean and standard deviation, corrected item-total correlation and reliability. Moreover, differences between the provided norm data and our sample were calculated. Analyses showed that fathers reported significantly higher levels of parenting stress than mothers. Furthermore, fathers taking parental leave did not differ significantly from those who did not, regarding their level of education or their perceived parenting stress. In conclusion, as it stands right now, the EBI does not adequately measure parenting stress in fathers, and father specific norms are needed to properly assess their levels of parenting stress. The results concerning parenting stress and parental leave were thus inconclusive. Furthermore, since reducing parenting stress in fathers is beneficial for the child's development and the welfare of the parents, further studies focusing on fathers' parenting stress are needed.
Keyphrases
  • stress induced
  • healthcare
  • mental health
  • depressive symptoms
  • psychometric properties
  • climate change
  • deep learning
  • case control